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Tracking 3D objects in continuous point cloud data sequences is an important research topic for mobile robots: it allows
robots to monitor the environment and make decisions and adapt their motions according to the changes in the world. An
example of such a typical application is visual servoing, with its key challenge to estimate the three dimensional pose
of an object in real-time.

During his internship at Willow Garage, Ryohei Ueda from the JSK laboratory at University of Tokyo, worked on a novel 3D tracking library for the Point Cloud Library (PCL) project. The purpose of the library is to provide a comprehensive algorithmic base for the estimation of 3D object poses using Monte Carlo sampling techniques and for calculating the likelihood using combined weighted metrics for hyper-dimensional spaces including Cartesian data, colors, and surface normals. The libpcl_tracking library is optimized to perform computations in real-time, by employing multi CPU cores optimization, adaptive particle filtering (KLD sampling) and other modern techniques.

To find out more about Ryohei's work, please watch the video above. You can also read the slides below (download pdf) for more technical details.